This page has only limited features, please log in for full access.

Unclaimed
F. Tsai
Department of Civil Engineering, National Central University, Zhongli City, Taoyuan 320, Taiwan

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 06 January 2021 in ISPRS International Journal of Geo-Information
Reads 0
Downloads 0

The universal soil loss equation (USLE) is a widely used empirical model for estimating soil loss. Among the USLE model factors, the cover management factor (C-factor) is a critical factor that substantially impacts the estimation result. Assigning C-factor values according to a land-use/land-cover (LULC) map from field surveys is a typical traditional approach. However, this approach may have limitations caused by the difficulty and cost in conducting field surveys and updating the LULC map regularly, thus significantly affecting the feasibility of multi-temporal analysis of soil erosion. To address this issue, this study uses data mining to build a random forest (RF) model between eight geospatial factors and the C-factor for the Shihmen Reservoir watershed in northern Taiwan for multi-temporal estimation of soil loss. The eight geospatial factors were collected or derived from remotely sensed images taken in 2004, a digital elevation model, and related digital maps. Due to the memory size limitation of the R software, only 4% of the total data points (population dataset) in each C-factor class were selected as the sample dataset (input dataset) for analysis using the stratified random sampling method. Seventy percent of the input dataset was used to train the RF model, and the other 30% was used to test the model. The results show that the RF model could capture the trend of vegetation recovery and soil loss reduction after the destructive event of Typhoon Aere in 2004 for multi-temporal analysis. Although the RF model was biased by the majority class’s large sample size (C = 0.01 class), the estimated soil erosion rate was close to the measurement obtained by the erosion pins installed in the watershed (90.6 t/ha-year). After the model’s completion, we furthered our aim to address the input dataset’s imbalanced data problem to improve the model’s classification performance. An ad-hoc down-sampling of the majority class technique was used to reduce the majority class’s sampling rate to 2%, 1%, and 0.5% while keeping the other minority classes at a 4% sample rate. The results show an improvement of the Kappa coefficient from 0.574 to 0.732, the AUC from 0.780 to 0.891, and the true positive rate of all minority classes combined from 0.43 to 0.70. However, the overall accuracy decreases from 0.952 to 0.846, and the true positive rate of the majority class declines from 0.99 to 0.94. The best average C-factor was achieved when the sampling rate of the majority class was 1%. On the other hand, the best soil erosion estimate was obtained when the sampling rate was 2%.

ACS Style

Fuan Tsai; Jhe-Syuan Lai; Kieu Anh Nguyen; Walter Chen. Determining Cover Management Factor with Remote Sensing and Spatial Analysis for Improving Long-Term Soil Loss Estimation in Watersheds. ISPRS International Journal of Geo-Information 2021, 10, 19 .

AMA Style

Fuan Tsai, Jhe-Syuan Lai, Kieu Anh Nguyen, Walter Chen. Determining Cover Management Factor with Remote Sensing and Spatial Analysis for Improving Long-Term Soil Loss Estimation in Watersheds. ISPRS International Journal of Geo-Information. 2021; 10 (1):19.

Chicago/Turabian Style

Fuan Tsai; Jhe-Syuan Lai; Kieu Anh Nguyen; Walter Chen. 2021. "Determining Cover Management Factor with Remote Sensing and Spatial Analysis for Improving Long-Term Soil Loss Estimation in Watersheds." ISPRS International Journal of Geo-Information 10, no. 1: 19.

Journal article
Published: 17 June 2020 in Remote Sensing
Reads 0
Downloads 0

The 2019 International Symposium on Remote Sensing (ISRS-2019) took place in Taipei, Taiwan from 17 to 19 April 2019. ISRS is one of the distinguished conferences on the photogrammetry, remote sensing and spatial information sciences, especially in East Asia. More than 220 papers were presented in 37 technical sessions organized at the conference. This Special Issue publishes a limited number of featured peer-reviewed papers extended from their original contributions at ISRS-2019. The selected papers highlight a variety of topics pertaining to innovative concepts, algorithms and applications with geospatial sensors, systems, and data, in conjunction with emerging technologies such as artificial intelligence, machine leaning and advanced spatial analysis algorithms. The topics of the selected papers include the following: the on-orbit radiometric calibration of satellite optical sensors, environmental characteristics assessment with remote sensing, machine learning-based photogrammetry and image analysis, and the integration of remote sensing and spatial analysis. The selected contributions also demonstrate and discuss various sophisticated applications in utilizing remote sensing, geospatial data, and technologies to address different environmental and societal issues. Readers should find the Special Issue enlightening and insightful for understanding state-of-the-art remote sensing and spatial information science research, development and applications.

ACS Style

Fuan Tsai; Chao-Hung Lin; Walter W. Chen; Jen-Jer Jaw; Kuo-Hsin Tseng. Editorial for the Special Issue on Selected Papers from the “2019 International Symposium on Remote Sensing”. Remote Sensing 2020, 12, 1 .

AMA Style

Fuan Tsai, Chao-Hung Lin, Walter W. Chen, Jen-Jer Jaw, Kuo-Hsin Tseng. Editorial for the Special Issue on Selected Papers from the “2019 International Symposium on Remote Sensing”. Remote Sensing. 2020; 12 (12):1.

Chicago/Turabian Style

Fuan Tsai; Chao-Hung Lin; Walter W. Chen; Jen-Jer Jaw; Kuo-Hsin Tseng. 2020. "Editorial for the Special Issue on Selected Papers from the “2019 International Symposium on Remote Sensing”." Remote Sensing 12, no. 12: 1.

Journal article
Published: 11 November 2019 in Remote Sensing
Reads 0
Downloads 0

A new Taiwanese satellite, FORMOSAT-5 (FS-5), with a payload remote sensing instrument (RSI) was launched in August 2017 to continue the mission of its predecessor FORMOSAT-2 (FS-2). Similar to FS-2, the RSI provides 2-m resolution panchromatic and 4-m resolution multi-spectral images as the primary payload on FS-5. However, the radiometric properties of the optical sensor may vary, based on the environment and time after being launched into the space. Thus, maintaining the radiometric quality of FS-5 RSI imagery is essential and significant to scientific research and further applications. Therefore, the objective of this study aimed at the on-orbit absolute radiometric assessment and calibration of on-orbit FS-5 RSI observations. Two renowned approaches, vicarious calibrations and cross-calibrations, were conducted at two calibration sites that employ a stable atmosphere and high surface reflectance, namely, Alkali Lake and Railroad Valley Playa in North America. For cross-calibrations, the Landsat-8 Operational Land Imager (LS-8 OLI) was selected as the reference. A second simulation of the satellite signal in the solar spectrum (6S) radiative transfer model was performed to compute the surface reflectance, atmospheric effects, and path radiance for the radiometric intensity at the top of the atmosphere. Results of vicarious calibrations from 11 field experiments demonstrated high consistency with those of seven case examinations of cross-calibration in terms of physical gain in spectra, implying that the practicality of the proposed approaches is high. Moreover, the multi-temporal results illustrated that RSI decay in optical sensitivity was evident after launch. The variation in the calibration coefficient of each band showed no obvious consistency (6%–24%) in 2017, but it tended to be stable at the order of 3%–5% of variation in most spectral bands during 2018. The results strongly suggest that periodical calibration is required and essential for further scientific applications.

ACS Style

Tang-Huang Lin; Jui-Chung Chang; Kuo-Hsien Hsu; Yun-Shan Lee; Sheng-Kai Zeng; Gin-Rong Liu; Fu-An Tsai; Hai-Po Chan. Radiometric Variations of On-Orbit FORMOSAT-5 RSI from Vicarious and Cross-Calibration Measurements. Remote Sensing 2019, 11, 2634 .

AMA Style

Tang-Huang Lin, Jui-Chung Chang, Kuo-Hsien Hsu, Yun-Shan Lee, Sheng-Kai Zeng, Gin-Rong Liu, Fu-An Tsai, Hai-Po Chan. Radiometric Variations of On-Orbit FORMOSAT-5 RSI from Vicarious and Cross-Calibration Measurements. Remote Sensing. 2019; 11 (22):2634.

Chicago/Turabian Style

Tang-Huang Lin; Jui-Chung Chang; Kuo-Hsien Hsu; Yun-Shan Lee; Sheng-Kai Zeng; Gin-Rong Liu; Fu-An Tsai; Hai-Po Chan. 2019. "Radiometric Variations of On-Orbit FORMOSAT-5 RSI from Vicarious and Cross-Calibration Measurements." Remote Sensing 11, no. 22: 2634.

Journal article
Published: 05 September 2019 in ISPRS International Journal of Geo-Information
Reads 0
Downloads 0

This study explores two modeling issues that may cause uncertainty in landslide susceptibility assessments when different sampling strategies are employed. The first issue is that extracted attributes within a landslide inventory polygon can vary if the sample is obtained from different locations with diverse topographic conditions. The second issue is the mixing problem of landslide inventory that the detection of landslide areas from remotely-sensed data generally includes source and run-out features unless the run-out portion can be removed manually with auxiliary data. To this end, different statistical sampling strategies and the run-out influence on random forests (RF)-based landslide susceptibility modeling are explored for Typhoon Morakot in 2009 in southern Taiwan. To address the construction of models with an extremely high false alarm error or missing error, this study integrated cost-sensitive analysis with RF to adjust the decision boundary to achieve improvements. Experimental results indicate that, compared with a logistic regression model, RF with the hybrid sample strategy generally performs better, achieving over 80% and 0.7 for the overall accuracy and kappa coefficient, respectively, and higher accuracies can be obtained when the run-out is treated as an independent class or combined with a non-landslide class. Cost-sensitive analysis significantly improved the prediction accuracy from 5% to 10%. Therefore, run-out should be separated from the landslide source and labeled as an individual class when preparing a landslide inventory.

ACS Style

Jhe-Syuan Lai; Shou-Hao Chiang; Fuan Tsai. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling. ISPRS International Journal of Geo-Information 2019, 8, 397 .

AMA Style

Jhe-Syuan Lai, Shou-Hao Chiang, Fuan Tsai. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling. ISPRS International Journal of Geo-Information. 2019; 8 (9):397.

Chicago/Turabian Style

Jhe-Syuan Lai; Shou-Hao Chiang; Fuan Tsai. 2019. "Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling." ISPRS International Journal of Geo-Information 8, no. 9: 397.

Journal article
Published: 27 August 2019 in Sensors
Reads 0
Downloads 0

This study developed a systematic approach with machine learning (ML) to apply the satellite remote sensing images, geographic information system (GIS) datasets, and spatial analysis for multi-temporal and event-based landslide susceptibility assessments at a regional scale. Random forests (RF) algorithm, one of the ML-based methods, was selected to construct the landslide susceptibility models. Different ratios of landslide and non-landslide samples were considered in the experiments. This study also employed a cost-sensitive analysis to adjust the decision boundary of the developed RF models with unbalanced sample ratios to improve the prediction results. Two strategies were investigated for model verification, namely space- and time-robustness. The space-robustness verification was designed for separating samples into training and examining data based on a single event or the same dataset. The time-robustness verification was designed for predicting subsequent landslide events by constructing a landslide susceptibility model based on a specific event or period. A total of 14 GIS-based landslide-related factors were used and derived from the spatial analyses. The developed landslide susceptibility models were tested in a watershed region in northern Taiwan with a landslide inventory of changes detected through multi-temporal satellite images and verified through field investigation. To further examine the developed models, the landslide susceptibility distributions of true occurrence samples and the generated landslide susceptibility maps were compared. The experiments demonstrated that the proposed method can provide more reasonable results, and the accuracies were found to be higher than 93% and 75% in most cases for space- and time-robustness verifications, respectively. In addition, the mapping results revealed that the multi-temporal models did not seem to be affected by the sample ratios included in the analyses.

ACS Style

Jhe-Syuan Lai; Fuan Tsai. Improving GIS-based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning. Sensors 2019, 19, 3717 .

AMA Style

Jhe-Syuan Lai, Fuan Tsai. Improving GIS-based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning. Sensors. 2019; 19 (17):3717.

Chicago/Turabian Style

Jhe-Syuan Lai; Fuan Tsai. 2019. "Improving GIS-based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning." Sensors 19, no. 17: 3717.

Journal article
Published: 03 October 2018 in Water
Reads 0
Downloads 0

Soil erosion is a global problem that will become worse as a result of climate change. While many parts of the world are speculating about the effect of increased rainfall intensity and frequency on soil erosion, Taiwan’s mountainous areas are already facing the power of rainfall erosivity more than six times the global average. To improve the modeling ability of extreme rainfall conditions on highly rugged terrains, we use two analysis units to simulate soil erosion at the Shihmen reservoir watershed in northern Taiwan. The first one is the grid cell method, which divides the study area into 10 m by 10 m grid cells. The second one is the slope unit method, which divides the study area using natural breaks in landform. We compared the modeling results with field measurements of erosion pins. To our surprise, the grid cell method is much more accurate in predicting soil erosion than the slope unit method, although the slope unit method resembles the real terrains much better than the grid cell method. The average erosion pin measurement is 6.5 mm in the Shihmen reservoir watershed, which is equivalent to 90.6 t ha−1 yr−1 of soil erosion.

ACS Style

Yi-Hsin Liu; Dong-Huang Li; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang; Fuan Tsai. Soil Erosion Modeling and Comparison Using Slope Units and Grid Cells in Shihmen Reservoir Watershed in Northern Taiwan. Water 2018, 10, 1387 .

AMA Style

Yi-Hsin Liu, Dong-Huang Li, Walter Chen, Bor-Shiun Lin, Uma Seeboonruang, Fuan Tsai. Soil Erosion Modeling and Comparison Using Slope Units and Grid Cells in Shihmen Reservoir Watershed in Northern Taiwan. Water. 2018; 10 (10):1387.

Chicago/Turabian Style

Yi-Hsin Liu; Dong-Huang Li; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang; Fuan Tsai. 2018. "Soil Erosion Modeling and Comparison Using Slope Units and Grid Cells in Shihmen Reservoir Watershed in Northern Taiwan." Water 10, no. 10: 1387.

Journal article
Published: 05 July 2018 in Ecological Engineering
Reads 0
Downloads 0

Soil loss due to sheet or rill soil erosion is a critical problem in watersheds of Taiwan. However, an order-of-magnitude discrepancy of soil loss in the literature raises many questions. In this study, we conducted a new analysis using the most recent available data and the Universal Soil Loss Equation (USLE) to compute the amounts of sheet and rill erosion of the Shihmen reservoir watershed in northern Taiwan. Using four different Digital Elevation Models (DEMs), we identified relatively high soil erosion sites and found them to be located at similar locations despite of the difference in DEM. We also determined that the average soil erosion in the Shihmen reservoir watershed is comparable to other watersheds in Asia, but higher than those of the European Union. Furthermore, soil erosion is not uniformly distributed throughout the study area. It is found that the distribution of soil erosion is highly skewed to the right (right-tailed), which means that the majority of the distribution is concentrated to the left side (many cells with low soil erosion). Based on our model, approximately 2% of the areas account for 30% of the soil erosion. In other words, a small proportion of the areas contribute to a large proportion of the total soil loss. Moreover, the DEM created from airborne LiDAR yields the highest amount of soil erosion, the two DEMs created from satellite images yield the lowest amounts of soil erosion, and the DEM created from aerial photographs yields an in-between soil erosion amount. Their vertical resolutions range from high to low. It appears that the amount of soil erosion is influenced by the vertical accuracy of DEMs. In addition to the comparison of DEMs, we demonstrated rudimentary steps to visualize areas of high soil erosion risk using freely available tool for long-term monitoring.

ACS Style

Walter Chen; Dong-Huang Li; Kai-Jie Yang; Fuan Tsai; Uma Seeboonruang. Identifying and comparing relatively high soil erosion sites with four DEMs. Ecological Engineering 2018, 120, 449 -463.

AMA Style

Walter Chen, Dong-Huang Li, Kai-Jie Yang, Fuan Tsai, Uma Seeboonruang. Identifying and comparing relatively high soil erosion sites with four DEMs. Ecological Engineering. 2018; 120 ():449-463.

Chicago/Turabian Style

Walter Chen; Dong-Huang Li; Kai-Jie Yang; Fuan Tsai; Uma Seeboonruang. 2018. "Identifying and comparing relatively high soil erosion sites with four DEMs." Ecological Engineering 120, no. : 449-463.

Journal article
Published: 21 June 2018 in Sensors
Reads 0
Downloads 0

Radiometric calibration for imaging sensors is a crucial procedure to ensure imagery quality. One of the challenges in relative radiometric calibration is to correct detector-level artifacts due to the fluctuation in discrepant responses (spatial) and electronic instability (temporal). In this paper, the integration of the empirical mode decomposition (EMD) with Hilbert–Huang transform (HHT) in relative radiometric calibration was explored for a new sensor, FS-5 RSI (remote sensing instrument onboard the FORMOSAT-5 satellite). The key intrinsic mode functions (IMFs) analyzed by HHT were examined with the pre-flight datasets of the FS-5 RSI in temporal and spatial variations. The results show that the EMD–HHT method can stabilize and improve the radiometric quality of the FS-5 imagery as well as boost its application ability to a new level. It is noticed that the IMFs of the spatial variation would be disturbed by the instability of the temporal variation. The relative response discrepancies among detector chips can be well calibrated after considering the temporal effect. Taking a test imagery dataset of gain setting G2 as an example, the standard deviation (STD) of the discrepancy in the digital number after calibration was dramatically scaled down compared to the original ones (e.g., PAN: 66.31 to 1.85; B1: 54.19 to 1.90; B2: 36.50 to 1.49; B3: 32.43 to 1.56; B4: 37.67 to 1.20). The good performance of pre-flight imagery indicates that the EMD–HHT approach could be highly practical to the on-orbit relative radiometric calibration of the FS-5 RSI sensor and is applicable to other optical sensors. To our knowledge, the proposed EMD–HHT approach is used for the first time to explore relative radiometric calibration for optical sensors.

ACS Style

Tang-Huang Lin; Min-Chung Hsiao; Hai-Po Chan; Fuan Tsai. A Novel Approach to Relative Radiometric Calibration on Spatial and Temporal Variations for FORMOSAT-5 RSI Imagery. Sensors 2018, 18, 1996 .

AMA Style

Tang-Huang Lin, Min-Chung Hsiao, Hai-Po Chan, Fuan Tsai. A Novel Approach to Relative Radiometric Calibration on Spatial and Temporal Variations for FORMOSAT-5 RSI Imagery. Sensors. 2018; 18 (7):1996.

Chicago/Turabian Style

Tang-Huang Lin; Min-Chung Hsiao; Hai-Po Chan; Fuan Tsai. 2018. "A Novel Approach to Relative Radiometric Calibration on Spatial and Temporal Variations for FORMOSAT-5 RSI Imagery." Sensors 18, no. 7: 1996.

Journal article
Published: 27 December 2017 in Sensors
Reads 0
Downloads 0

This paper describes a flexible camera calibration method using refined vanishing points without prior information. Vanishing points are estimated from human-made features like parallel lines and repeated patterns. With the vanishing points extracted from the three mutually orthogonal directions, the interior and exterior orientation parameters can be further calculated using collinearity condition equations. A vanishing point refinement process is proposed to reduce the uncertainty caused by vanishing point localization errors. The fine-tuning algorithm is based on the divergence of grouped feature points projected onto the reference plane, minimizing the standard deviation of each of the grouped collinear points with an O(1) computational complexity. This paper also presents an automated vanishing point estimation approach based on the cascade Hough transform. The experiment results indicate that the vanishing point refinement process can significantly improve camera calibration parameters and the root mean square error (RMSE) of the constructed 3D model can be reduced by about 30%.

ACS Style

Huan Chang; Fuan Tsai. Vanishing Point Extraction and Refinement for Robust Camera Calibration. Sensors 2017, 18, 63 .

AMA Style

Huan Chang, Fuan Tsai. Vanishing Point Extraction and Refinement for Robust Camera Calibration. Sensors. 2017; 18 (1):63.

Chicago/Turabian Style

Huan Chang; Fuan Tsai. 2017. "Vanishing Point Extraction and Refinement for Robust Camera Calibration." Sensors 18, no. 1: 63.

Journal article
Published: 21 August 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Reads 0
Downloads 0

This study developed a simple but effective strategy to combine 3D volume and mesh models for representing complicated heritage buildings and structures. The idea is to seamlessly integrate 3D parametric or polyhedral models and mesh-based digital surfaces to generate a hybrid 3D model that can take advantages of both modeling methods. The proposed hybrid model generation framework is separated into three phases. Firstly, after acquiring or generating 3D point clouds of the target, these 3D points are partitioned into different groups. Secondly, a parametric or polyhedral model of each group is generated based on plane and surface fitting algorithms to represent the basic structure of that region. A “bare-bones” model of the target can subsequently be constructed by connecting all 3D volume element models. In the third phase, the constructed bare-bones model is used as a mask to remove points enclosed by the bare-bones model from the original point clouds. The remaining points are then connected to form 3D surface mesh patches. The boundary points of each surface patch are identified and these boundary points are projected onto the surfaces of the bare-bones model. Finally, new meshes are created to connect the projected points and original mesh boundaries to integrate the mesh surfaces with the 3D volume model. The proposed method was applied to an open-source point cloud data set and point clouds of a local historical structure. Preliminary results indicated that the reconstructed hybrid models using the proposed method can retain both fundamental 3D volume characteristics and accurate geometric appearance with fine details. The reconstructed hybrid models can also be used to represent targets in different levels of detail according to user and system requirements in different applications.

ACS Style

F. Tsai; H. Chang; Y.-W. Lin. COMBINING 3D VOLUME AND MESH MODELS FOR REPRESENTING COMPLICATED HERITAGE BUILDINGS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-2/W5, 673 -677.

AMA Style

F. Tsai, H. Chang, Y.-W. Lin. COMBINING 3D VOLUME AND MESH MODELS FOR REPRESENTING COMPLICATED HERITAGE BUILDINGS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-2/W5 ():673-677.

Chicago/Turabian Style

F. Tsai; H. Chang; Y.-W. Lin. 2017. "COMBINING 3D VOLUME AND MESH MODELS FOR REPRESENTING COMPLICATED HERITAGE BUILDINGS." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W5, no. : 673-677.

Journal article
Published: 15 November 2016 in Journal of Communications and Networks
Reads 0
Downloads 0

Providing a customer with tailored location-based services (LBSs) is a fundamental problem. For location-estimation techniques with radio-based measurements, LBS applications are widely available for mobile devices (MDs), such as smartphones, enabling users to run multi-task applications. LBS information not only enables obtaining the current location of an MD but also pro- vides real-time push-pull communication service. For indoor environments, localization technologies based on radio frequency (RF) pattern-matching approaches are accurate and commonly used. However, to survey radio information for pattern-matching approaches, a considerable amount of time and work is spent in indoor environments. Consequently, in order to reduce the system-deployment cost and computing complexity, this article proposes an indoor positioning approach, which involves using Asus Xtion to facilitate capturing RF signals during an offline site survey. The depth information obtained using Asus Xtion is utilized to estimate the locations and predict the received signal strength (RF information) at uncertain locations. The proposed approach effectively reduces not only the time and work costs but also the computing complexity involved in determining the orientation and RF during the online positioning phase by estimating the user's location by using a smartphone. The experimental results demonstrated that more than 78% of time was saved, and the number of samples acquired using the proposed method during the offline phase was twice as much as that acquired using the conventional method. For the on- line phase, the location estimates have error distances of less than 2.67 m. Therefore, the proposed approach is beneficial for use in various LBS applications.

ACS Style

Sheng-Cheng Yeh; Yih-Shyh Chiou; Huan Chang; Wang-Hsin Hsu; Shiau-Huang Liu; Fuan Tsai. Performance improvement of offline phase for indoor positioning systems using Asus Xtion and smartphone sensors. Journal of Communications and Networks 2016, 18, 837 -845.

AMA Style

Sheng-Cheng Yeh, Yih-Shyh Chiou, Huan Chang, Wang-Hsin Hsu, Shiau-Huang Liu, Fuan Tsai. Performance improvement of offline phase for indoor positioning systems using Asus Xtion and smartphone sensors. Journal of Communications and Networks. 2016; 18 (5):837-845.

Chicago/Turabian Style

Sheng-Cheng Yeh; Yih-Shyh Chiou; Huan Chang; Wang-Hsin Hsu; Shiau-Huang Liu; Fuan Tsai. 2016. "Performance improvement of offline phase for indoor positioning systems using Asus Xtion and smartphone sensors." Journal of Communications and Networks 18, no. 5: 837-845.

Journal article
Published: 22 June 2016 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Reads 0
Downloads 0

This study implements a data mining-based algorithm, the random forests classifier, with geo-spatial data to construct a regional and rainfall-induced landslide susceptibility model. The developed model also takes account of landslide regions (source, non-occurrence and run-out signatures) from the original landslide inventory in order to increase the reliability of the susceptibility modelling. A total of ten causative factors were collected and used in this study, including aspect, curvature, elevation, slope, faults, geology, NDVI (Normalized Difference Vegetation Index), rivers, roads and soil data. Consequently, this study transforms the landslide inventory and vector-based causative factors into the pixel-based format in order to overlay with other raster data for constructing the random forests based model. This study also uses original and edited topographic data in the analysis to understand their impacts to the susceptibility modeling. Experimental results demonstrate that after identifying the run-out signatures, the overall accuracy and Kappa coefficient have been reached to be become more than 85 % and 0.8, respectively. In addition, correcting unreasonable topographic feature of the digital terrain model also produces more reliable modelling results.

ACS Style

J.-S. Lai; F. Tsai; S.-H. Chiang. INTEGRATING GEO-SPATIAL DATA FOR REGIONAL LANDSLIDE SUSCEPTIBILITY MODELING IN CONSIDERATION OF RUN-OUT SIGNATURE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B8, 89 -93.

AMA Style

J.-S. Lai, F. Tsai, S.-H. Chiang. INTEGRATING GEO-SPATIAL DATA FOR REGIONAL LANDSLIDE SUSCEPTIBILITY MODELING IN CONSIDERATION OF RUN-OUT SIGNATURE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B8 ():89-93.

Chicago/Turabian Style

J.-S. Lai; F. Tsai; S.-H. Chiang. 2016. "INTEGRATING GEO-SPATIAL DATA FOR REGIONAL LANDSLIDE SUSCEPTIBILITY MODELING IN CONSIDERATION OF RUN-OUT SIGNATURE." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8, no. : 89-93.

Journal article
Published: 17 June 2016 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Reads 0
Downloads 0

With the development of technology, UAS is an advance technology to support rapid mapping for disaster response. The aim of this study is to develop educational modules for UAS data processing in rapid 3D mapping. The designed modules for this study are focused on UAV data processing from available freeware or trial software for education purpose. The key modules include orientation modelling, 3D point clouds generation, image georeferencing and visualization. The orientation modelling modules adopts VisualSFM to determine the projection matrix for each image station. Besides, the approximate ground control points are measured from OpenStreetMap for absolute orientation. The second module uses SURE and the orientation files from previous module for 3D point clouds generation. Then, the ground point selection and digital terrain model generation can be archived by LAStools. The third module stitches individual rectified images into a mosaic image using Microsoft ICE (Image Composite Editor). The last module visualizes and measures the generated dense point clouds in CloudCompare. These comprehensive UAS processing modules allow the students to gain the skills to process and deliver UAS photogrammetric products in rapid 3D mapping. Moreover, they can also apply the photogrammetric products for analysis in practice.

ACS Style

Tee-Ann Teo; Peter Tian-Yuan Shih; Sz-Cheng Yu; Fuan Tsai. THE USE OF UAS FOR RAPID 3D MAPPING IN GEOMATICS EDUCATION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B6, 95 -100.

AMA Style

Tee-Ann Teo, Peter Tian-Yuan Shih, Sz-Cheng Yu, Fuan Tsai. THE USE OF UAS FOR RAPID 3D MAPPING IN GEOMATICS EDUCATION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B6 ():95-100.

Chicago/Turabian Style

Tee-Ann Teo; Peter Tian-Yuan Shih; Sz-Cheng Yu; Fuan Tsai. 2016. "THE USE OF UAS FOR RAPID 3D MAPPING IN GEOMATICS EDUCATION." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B6, no. : 95-100.

Journal article
Published: 17 June 2016 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Reads 0
Downloads 0

The Asian Association on Remote Sensing (AARS) organizes a web contest (WEBCON) of photogrammetry, remote sensing and spatial information sciences in the annual meeting of Asian Conference on Remote Sensing (ACRS) every year. The purpose of WEBCON is to promote the development of web and other forms of internet services of the internet related to geo-information sciences and to attract more students and young scientists participating in the related fields of study and applications. Since 2011, WEBCON has become one of the major events in ACRS and successfully increased the interest in the research, development and applications of photogrammetry, remote sensing and spatial information sciences among students and young scientist. The success of WEBCON is an excellent example of promoting the profession of spatial information to young people.

ACS Style

F. Tsai; K. Cho. CONTEST OF WEB-BASED GEOSPATIAL APPLICATIONS FOR STUDENTS AND YOUNG SCIENTISTS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B6, 101 -103.

AMA Style

F. Tsai, K. Cho. CONTEST OF WEB-BASED GEOSPATIAL APPLICATIONS FOR STUDENTS AND YOUNG SCIENTISTS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B6 ():101-103.

Chicago/Turabian Style

F. Tsai; K. Cho. 2016. "CONTEST OF WEB-BASED GEOSPATIAL APPLICATIONS FOR STUDENTS AND YOUNG SCIENTISTS." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B6, no. : 101-103.

Journal article
Published: 17 June 2016 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Reads 0
Downloads 0

With the development of technology, UAS is an advance technology to support rapid mapping for disaster response. The aim of this study is to develop educational modules for UAS data processing in rapid 3D mapping. The designed modules for this study are focused on UAV data processing from available freeware or trial software for education purpose. The key modules include orientation modelling, 3D point clouds generation, image georeferencing and visualization. The orientation modelling modules adopts VisualSFM to determine the projection matrix for each image station. Besides, the approximate ground control points are measured from OpenStreetMap for absolute orientation. The second module uses SURE and the orientation files from previous module for 3D point clouds generation. Then, the ground point selection and digital terrain model generation can be archived by LAStools. The third module stitches individual rectified images into a mosaic image using Microsoft ICE (Image Composite Editor). The last module visualizes and measures the generated dense point clouds in CloudCompare. These comprehensive UAS processing modules allow the students to gain the skills to process and deliver UAS photogrammetric products in rapid 3D mapping. Moreover, they can also apply the photogrammetric products for analysis in practice.

ACS Style

Tee-Ann Teo; Peter Tian-Yuan Shih; Sz-Cheng Yu; Fuan Tsai. THE USE OF UAS FOR RAPID 3D MAPPING IN GEOMATICS EDUCATION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B6, 95 -100.

AMA Style

Tee-Ann Teo, Peter Tian-Yuan Shih, Sz-Cheng Yu, Fuan Tsai. THE USE OF UAS FOR RAPID 3D MAPPING IN GEOMATICS EDUCATION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B6 ():95-100.

Chicago/Turabian Style

Tee-Ann Teo; Peter Tian-Yuan Shih; Sz-Cheng Yu; Fuan Tsai. 2016. "THE USE OF UAS FOR RAPID 3D MAPPING IN GEOMATICS EDUCATION." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B6, no. : 95-100.

Journal article
Published: 11 May 2015 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Reads 0
Downloads 0

This paper presents a data acquisition system consisting of multiple RGB-D sensors and digital single-lens reflex (DSLR) cameras. A systematic data processing procedure for integrating these two kinds of devices to generate three-dimensional point clouds of indoor environments is also developed and described. In the developed system, DSLR cameras are used to bridge the Kinects and provide a more accurate ray intersection condition, which takes advantage of the higher resolution and image quality of the DSLR cameras. Structure from Motion (SFM) reconstruction is used to link and merge multiple Kinect point clouds and dense point clouds (from DSLR color images) to generate initial integrated point clouds. Then, bundle adjustment is used to resolve the exterior orientation (EO) of all images. Those exterior orientations are used as the initial values to combine these point clouds at each frame into the same coordinate system using Helmert (seven-parameter) transformation. Experimental results demonstrate that the design of the data acquisition system and the data processing procedure can generate dense and fully colored point clouds of indoor environments successfully even in featureless areas. The accuracy of the generated point clouds were evaluated by comparing the widths and heights of identified objects as well as coordinates of pre-set independent check points against in situ measurements. Based on the generated point clouds, complete and accurate three-dimensional models of indoor environments can be constructed effectively.

ACS Style

F. Tsai; T.-S. Wu; I.-C. Lee; H. Chang; A. Y. S. Su. RECONSTRUCTION OF INDOOR MODELS USING POINT CLOUDS GENERATED FROM SINGLE-LENS REFLEX CAMERAS AND DEPTH IMAGES. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2015, XL-4/W5, 99 -102.

AMA Style

F. Tsai, T.-S. Wu, I.-C. Lee, H. Chang, A. Y. S. Su. RECONSTRUCTION OF INDOOR MODELS USING POINT CLOUDS GENERATED FROM SINGLE-LENS REFLEX CAMERAS AND DEPTH IMAGES. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2015; XL-4/W5 ():99-102.

Chicago/Turabian Style

F. Tsai; T.-S. Wu; I.-C. Lee; H. Chang; A. Y. S. Su. 2015. "RECONSTRUCTION OF INDOOR MODELS USING POINT CLOUDS GENERATED FROM SINGLE-LENS REFLEX CAMERAS AND DEPTH IMAGES." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W5, no. : 99-102.

Journal article
Published: 01 May 2015 in Applied Mechanics and Materials
Reads 0
Downloads 0

Robust and accurate positioning systems with seamless outdoor and indoor coverage have been receiving a great deal of attention. In outdoor environments, as radio signals transmitted from base stations or satellites are jammed or shielded, to estimate an accurate location using only the absolute positioning scheme remains a difficult problem regarding location accuracy. In order to overcome the drawback of the ranging scheme based on the radio signals, this paper presents a positioning approach based on inertial-measurement-unit (IMU) observations to estimate the location of a mobile terminal (MT). For the location-estimation technique, the positioning experiment is handled by the dead-reckoning (DR) algorithm. By processing the observations from the IMU, it is possible to estimate the movement of an MT (car). In terms of the IMU-based approach, although the experimental results demonstrate that the positioning scheme using the DR algorithm causes the error propagation, the approach can work in short period of time for navigation applications.

ACS Style

Yih Shyh Chiou; Fuan Tsai. Localization Experiments Based on Inertial Sensors for Navigation Applications. Applied Mechanics and Materials 2015, 764-765, 550 -554.

AMA Style

Yih Shyh Chiou, Fuan Tsai. Localization Experiments Based on Inertial Sensors for Navigation Applications. Applied Mechanics and Materials. 2015; 764-765 ():550-554.

Chicago/Turabian Style

Yih Shyh Chiou; Fuan Tsai. 2015. "Localization Experiments Based on Inertial Sensors for Navigation Applications." Applied Mechanics and Materials 764-765, no. : 550-554.

Journal article
Published: 01 September 2014 in Photogrammetric Engineering & Remote Sensing
Reads 0
Downloads 0
ACS Style

Kunihiko Yoshino; Sayuri Kawaguchi; Fusayuki Kanda; Keiji Kushida; Fuan Tsai. Very High Resolution Plant Community Mapping at High Moor, Kushiro Wetland. Photogrammetric Engineering & Remote Sensing 2014, 80, 895 -905.

AMA Style

Kunihiko Yoshino, Sayuri Kawaguchi, Fusayuki Kanda, Keiji Kushida, Fuan Tsai. Very High Resolution Plant Community Mapping at High Moor, Kushiro Wetland. Photogrammetric Engineering & Remote Sensing. 2014; 80 (9):895-905.

Chicago/Turabian Style

Kunihiko Yoshino; Sayuri Kawaguchi; Fusayuki Kanda; Keiji Kushida; Fuan Tsai. 2014. "Very High Resolution Plant Community Mapping at High Moor, Kushiro Wetland." Photogrammetric Engineering & Remote Sensing 80, no. 9: 895-905.

Journal article
Published: 06 June 2014 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Reads 0
Downloads 0

This paper briefly presents two approaches for effective three-dimensional (3D) building model reconstruction from terrestrial laser scanning (TLS) data and single perspective view imagery and assesses their applicability to the reconstruction of 3D models of landmark or historical buildings. The collected LiDAR point clouds are registered based on conjugate points identified using a seven-parameter transformation system. Three dimensional models are generated using plan and surface fitting algorithms. The proposed single-view reconstruction (SVR) method is based on vanishing points and single-view metrology. More detailed models can also be generated according to semantic analysis of the façade images. Experimental results presented in this paper demonstrate that both TLS and SVR approaches can successfully produce accurate and detailed 3D building models from LiDAR point clouds or different types of single-view perspective images.

ACS Style

F. Tsai; H. Chang. Evaluations of Three-Dimensional Building Model Reconstruction from LiDAR Point Clouds and Single-View Perspective Imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2014, XL-5, 597 -600.

AMA Style

F. Tsai, H. Chang. Evaluations of Three-Dimensional Building Model Reconstruction from LiDAR Point Clouds and Single-View Perspective Imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2014; XL-5 ():597-600.

Chicago/Turabian Style

F. Tsai; H. Chang. 2014. "Evaluations of Three-Dimensional Building Model Reconstruction from LiDAR Point Clouds and Single-View Perspective Imagery." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5, no. : 597-600.

Journal article
Published: 23 April 2014 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Reads 0
Downloads 0

This paper presents an efficient location tracking algorithm that integrates vision-based motion estimation and IMU data. Orientation and translation parameters of the mobile device are estimated from video frames or highly overlapped image sequences acquired with built-in cameras of mobile devices. IMU data are used to maintain continuity of the orientation estimation between sampling of the image homography calculation. The developed algorithm consists of six primary steps: (1) pre-processing; (2) feature points detection and matching; (3) homography calculation; (4) control points detection and registration; (5) motion estimation and filtering; (6) IMU data integration. The pre-processing of the input video frames or images is to control the sampling rate and image resolution in order to increase the computing efficiency. The overlap rate between selected frames is designed to remain above 60 % for matching. After preprocessing, feature points will be extracted and matched between adjacent frames as the conjugate points. A perspective homography is constructed and used to map one image to another if the co-planar feature points between subsequent images are fully matched. The homography matrix can provide the camera orientation and translation parameters according to the conjugate pairs. An area-based image-matching method is employed to recognize landmarks as reference nodes (RNs). In addition, a filtering mechanism is proposed to ensure the rotation angle was correctly recorded and to increase the tracking accuracy. Comparisons of the trajectory results with different combinations among vision-based motion estimation, filtering mechanism and IMU data integration are evaluated thoroughly and the accuracy is validated with on-site measurement data. Experimental results indicate that the develop algorithm can effectively estimate the trajectory of moving mobile devices and can be used as a cost-effective alternative for LBS device both in outdoor and indoor environment.

ACS Style

F. Tsai; H. Chang; A. Y. S. Su. Combining MEMS-based IMU data and vision-based trajectory estimation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2014, XL-4, 267 -271.

AMA Style

F. Tsai, H. Chang, A. Y. S. Su. Combining MEMS-based IMU data and vision-based trajectory estimation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2014; XL-4 ():267-271.

Chicago/Turabian Style

F. Tsai; H. Chang; A. Y. S. Su. 2014. "Combining MEMS-based IMU data and vision-based trajectory estimation." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4, no. : 267-271.